Overview

Dataset statistics

Number of variables31
Number of observations57575
Missing cells108768
Missing cells (%)6.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory50.6 MiB
Average record size in memory922.1 B

Variable types

NUM15
CAT13
BOOL2
DATE1

Warnings

username has a high cardinality: 43554 distinct values High cardinality
country is highly correlated with languagecodeHigh correlation
languagecode is highly correlated with countryHigh correlation
nrofdependants has 39009 (67.8%) missing values Missing
workexperience has 38493 (66.9%) missing values Missing
previousearlyrepaymentsbeforeloan has 30802 (53.5%) missing values Missing
incometotal is highly skewed (γ1 = 117.1419579) Skewed
incomefromsalary is highly skewed (γ1 = 88.63885754) Skewed
incomefromothers is highly skewed (γ1 = 26.59696623) Skewed
liabilitiestotal is highly skewed (γ1 = 239.6549801) Skewed
username is uniformly distributed Uniform
loanid has unique values Unique
incomefromsalary has 39497 (68.6%) zeros Zeros
incomefromothers has 50943 (88.5%) zeros Zeros
existingliabilities has 14916 (25.9%) zeros Zeros
liabilitiestotal has 15413 (26.8%) zeros Zeros
debttoincome has 38935 (67.6%) zeros Zeros
noofpreviousloansbeforeloan has 43554 (75.6%) zeros Zeros
amountofpreviousloansbeforeloan has 43554 (75.6%) zeros Zeros
priorrepayments has 43733 (76.0%) zeros Zeros
previousearlyrepaymentsbeforeloan has 25245 (43.8%) zeros Zeros
previousearlyrepaymentscountbeforeloan has 55985 (97.2%) zeros Zeros

Reproduction

Analysis started2021-04-20 09:56:05.574367
Analysis finished2021-04-20 09:57:03.960258
Duration58.39 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

defaulted
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size449.9 KiB
0
44471 
1
13104 
ValueCountFrequency (%) 
04447177.2%
 
11310422.8%
 
2021-04-20T17:57:04.021825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

loanid
Categorical

UNIQUE

Distinct57575
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size449.9 KiB
87307247-5636-4E5D-8985-A62C00D2828D
 
1
2306213C-3661-4303-9C9B-AAB9001A9A13
 
1
1E55D8EC-FE8C-4F0A-9CCF-A9E90182E6B5
 
1
040B414A-7D80-4329-8CC1-A9A400CFD7DB
 
1
98E0A84D-1DFC-4EC1-B91B-AA7A00ED1844
 
1
Other values (57570)
57570 
ValueCountFrequency (%) 
87307247-5636-4E5D-8985-A62C00D2828D1< 0.1%
 
2306213C-3661-4303-9C9B-AAB9001A9A131< 0.1%
 
1E55D8EC-FE8C-4F0A-9CCF-A9E90182E6B51< 0.1%
 
040B414A-7D80-4329-8CC1-A9A400CFD7DB1< 0.1%
 
98E0A84D-1DFC-4EC1-B91B-AA7A00ED18441< 0.1%
 
FF7B4086-1D16-4273-8CEB-AAE200F0EB4F1< 0.1%
 
1D7C9467-7C78-4790-95AC-A40A00E9C1091< 0.1%
 
680174FE-2D96-40A3-A1FB-AA9C01491BB91< 0.1%
 
F0CE4ADA-D6A5-4F34-AB95-A98E00AE15DD1< 0.1%
 
6BDCE6F3-9DBE-4E33-BC93-AAF5014A46EB1< 0.1%
 
C9DE9C39-20F3-4074-855A-A9FA00CA09BF1< 0.1%
 
39212968-8524-4B2F-A268-A82000A8FA141< 0.1%
 
E9BC2347-18AE-4F39-AD97-A8BD00B28BB31< 0.1%
 
C8D1487E-6317-44D1-90A3-A5140124F8911< 0.1%
 
85489644-B986-4D39-8FED-A6D100C6E3F01< 0.1%
 
7FF15D23-E02D-4674-8FFB-A804012773801< 0.1%
 
9557F0FF-8E47-4308-9EED-A2D9008FFB601< 0.1%
 
42C65453-6391-4D10-85E6-AAC6010AA9D61< 0.1%
 
6C054AAF-AC67-4C95-83F6-AA65010DB4F41< 0.1%
 
8F08DBC9-3C4C-484E-8996-A78600A135E41< 0.1%
 
3219994D-2511-4535-A296-AADC00833F2A1< 0.1%
 
41BB8DB1-A77B-4DEE-AB11-AAF800FADD661< 0.1%
 
E8FD5160-B64B-48C5-AB6A-A7B6013C56571< 0.1%
 
197A2955-C91D-460B-945D-AA8500F6985F1< 0.1%
 
9B7EEBE7-2CC4-4F1D-BD4E-AA21012136FB1< 0.1%
 
Other values (57550)57550> 99.9%
 
2021-04-20T17:57:04.398691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique57575 ?
Unique (%)100.0%
2021-04-20T17:57:04.624695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length36
Mean length36
Min length36

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
-23030011.1%
 
01868449.0%
 
A1856879.0%
 
41520337.3%
 
11185425.7%
 
91150265.5%
 
B1118435.4%
 
81115775.4%
 
7978924.7%
 
3966614.7%
 
D961974.6%
 
C954824.6%
 
E954794.6%
 
2954564.6%
 
F954324.6%
 
6943744.6%
 
5938754.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number116228056.1%
 
Uppercase Letter68012032.8%
 
Dash Punctuation23030011.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A18568727.3%
 
B11184316.4%
 
D9619714.1%
 
C9548214.0%
 
E9547914.0%
 
F9543214.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
018684416.1%
 
415203313.1%
 
111854210.2%
 
91150269.9%
 
81115779.6%
 
7978928.4%
 
3966618.3%
 
2954568.2%
 
6943748.1%
 
5938758.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-230300100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common139258067.2%
 
Latin68012032.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A18568727.3%
 
B11184316.4%
 
D9619714.1%
 
C9548214.0%
 
E9547914.0%
 
F9543214.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
-23030016.5%
 
018684413.4%
 
415203310.9%
 
11185428.5%
 
91150268.3%
 
81115778.0%
 
7978927.0%
 
3966616.9%
 
2954566.9%
 
6943746.8%
 
5938756.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2072700100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
-23030011.1%
 
01868449.0%
 
A1856879.0%
 
41520337.3%
 
11185425.7%
 
91150265.5%
 
B1118435.4%
 
81115775.4%
 
7978924.7%
 
3966614.7%
 
D961974.6%
 
C954824.6%
 
E954794.6%
 
2954564.6%
 
F954324.6%
 
6943744.6%
 
5938754.5%
 

username
Categorical

HIGH CARDINALITY
UNIFORM

Distinct43554
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Memory size449.9 KiB
BOKKA6993
 
13
BO7763A73
 
13
BO5K51166
 
10
Siil23
 
9
anne48
 
9
Other values (43549)
57521 
ValueCountFrequency (%) 
BOKKA699313< 0.1%
 
BO7763A7313< 0.1%
 
BO5K5116610< 0.1%
 
Siil239< 0.1%
 
anne489< 0.1%
 
TAAWOKAS8< 0.1%
 
Kadri8< 0.1%
 
BO16K64218< 0.1%
 
BO23636727< 0.1%
 
BO3K34A3A7< 0.1%
 
BO7747227< 0.1%
 
BO46436A17< 0.1%
 
BO4195A917< 0.1%
 
raunooo7< 0.1%
 
13Maximus136< 0.1%
 
BO73K65916< 0.1%
 
BOA3643616< 0.1%
 
BO191A5K66< 0.1%
 
BO99116766< 0.1%
 
estdonk6< 0.1%
 
BO19K56916< 0.1%
 
BO63943A66< 0.1%
 
intzest5< 0.1%
 
uhhuu5< 0.1%
 
battleonn5< 0.1%
 
Other values (43529)5739299.7%
 
2021-04-20T17:57:04.923848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique32832 ?
Unique (%)57.0%
2021-04-20T17:57:05.140488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length9
Mean length8.802344768
Min length2

Overview of Unicode Properties

Unique unicode characters82
Unique unicode categories7 ?
Unique unicode scripts3 ?
Unique unicode blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
O5440010.7%
 
B5437610.7%
 
1425408.4%
 
6400707.9%
 
3394657.8%
 
7390117.7%
 
A386607.6%
 
2386267.6%
 
9374027.4%
 
4358627.1%
 
K326736.4%
 
5324256.4%
 
a27460.5%
 
i20040.4%
 
e17890.4%
 
r14600.3%
 
n13040.3%
 
l12010.2%
 
s11980.2%
 
k10600.2%
 
t10200.2%
 
o9500.2%
 
u8160.2%
 
m8100.2%
 
v4500.1%
 
Other values (57)44770.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number30597960.4%
 
Uppercase Letter18132635.8%
 
Lowercase Letter193363.8%
 
Space Separator131< 0.1%
 
Connector Punctuation9< 0.1%
 
Other Punctuation8< 0.1%
 
Dash Punctuation6< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a274614.2%
 
i200410.4%
 
e17899.3%
 
r14607.6%
 
n13046.7%
 
l12016.2%
 
s11986.2%
 
k10605.5%
 
t10205.3%
 
o9504.9%
 
u8164.2%
 
m8104.2%
 
v4502.3%
 
d4002.1%
 
j3161.6%
 
g2911.5%
 
p2861.5%
 
h2711.4%
 
b1891.0%
 
c1810.9%
 
y1770.9%
 
z1320.7%
 
x1240.6%
 
f680.4%
 
w410.2%
 
Other values (13)520.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
14254013.9%
 
64007013.1%
 
33946512.9%
 
73901112.7%
 
23862612.6%
 
93740212.2%
 
43586211.7%
 
53242510.6%
 
03280.1%
 
82500.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O5440030.0%
 
B5437630.0%
 
A3866021.3%
 
K3267318.0%
 
M1880.1%
 
R1160.1%
 
S1130.1%
 
T1110.1%
 
L1030.1%
 
E87< 0.1%
 
I61< 0.1%
 
V59< 0.1%
 
P58< 0.1%
 
N56< 0.1%
 
J55< 0.1%
 
D40< 0.1%
 
G34< 0.1%
 
H32< 0.1%
 
U23< 0.1%
 
F22< 0.1%
 
C16< 0.1%
 
W15< 0.1%
 
Y11< 0.1%
 
Z8< 0.1%
 
X3< 0.1%
 
Other values (4)6< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
131100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-6100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_9100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.675.0%
 
@225.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common30613360.4%
 
Latin20065039.6%
 
Cyrillic12< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
O5440027.1%
 
B5437627.1%
 
A3866019.3%
 
K3267316.3%
 
a27461.4%
 
i20041.0%
 
e17890.9%
 
r14600.7%
 
n13040.6%
 
l12010.6%
 
s11980.6%
 
k10600.5%
 
t10200.5%
 
o9500.5%
 
u8160.4%
 
m8100.4%
 
v4500.2%
 
d4000.2%
 
j3160.2%
 
g2910.1%
 
p2860.1%
 
h2710.1%
 
b1890.1%
 
M1880.1%
 
c1810.1%
 
Other values (32)16110.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
14254013.9%
 
64007013.1%
 
33946512.9%
 
73901112.7%
 
23862612.6%
 
93740212.2%
 
43586211.7%
 
53242510.6%
 
03280.1%
 
82500.1%
 
131< 0.1%
 
_9< 0.1%
 
-6< 0.1%
 
.6< 0.1%
 
@2< 0.1%
 

Most frequent Cyrillic characters

ValueCountFrequency (%) 
р216.7%
 
т216.7%
 
А18.3%
 
у18.3%
 
З18.3%
 
и18.3%
 
н18.3%
 
а18.3%
 
о18.3%
 
в18.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII506756> 99.9%
 
None27< 0.1%
 
Cyrillic12< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
O5440010.7%
 
B5437610.7%
 
1425408.4%
 
6400707.9%
 
3394657.8%
 
7390117.7%
 
A386607.6%
 
2386267.6%
 
9374027.4%
 
4358627.1%
 
K326736.4%
 
5324256.4%
 
a27460.5%
 
i20040.4%
 
e17890.4%
 
r14600.3%
 
n13040.3%
 
l12010.2%
 
s11980.2%
 
k10600.2%
 
t10200.2%
 
o9500.2%
 
u8160.2%
 
m8100.2%
 
v4500.1%
 
Other values (42)44380.9%
 

Most frequent None characters

ValueCountFrequency (%) 
ä1140.7%
 
õ933.3%
 
ü414.8%
 
Ü27.4%
 
ö13.7%
 

Most frequent Cyrillic characters

ValueCountFrequency (%) 
р216.7%
 
т216.7%
 
А18.3%
 
у18.3%
 
З18.3%
 
и18.3%
 
н18.3%
 
а18.3%
 
о18.3%
 
в18.3%
 
Distinct55810
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size449.9 KiB
Minimum2010-03-01 13:45:00
Maximum2019-12-03 12:58:00
2021-04-20T17:57:05.339614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:57:05.538527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

languagecode
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size449.9 KiB
4
18539 
1
18287 
6
15854 
3
4124 
2
 
471
Other values (8)
 
300
ValueCountFrequency (%) 
41853932.2%
 
11828731.8%
 
61585427.5%
 
341247.2%
 
24710.8%
 
92900.5%
 
224< 0.1%
 
131< 0.1%
 
151< 0.1%
 
51< 0.1%
 
101< 0.1%
 
211< 0.1%
 
71< 0.1%
 
2021-04-20T17:57:05.783868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6 ?
Unique (%)< 0.1%
2021-04-20T17:57:05.970643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.000138949
Min length1

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
41853932.2%
 
11829131.8%
 
61585427.5%
 
341257.2%
 
24800.8%
 
92900.5%
 
52< 0.1%
 
01< 0.1%
 
71< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number57583100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
41853932.2%
 
11829131.8%
 
61585427.5%
 
341257.2%
 
24800.8%
 
92900.5%
 
52< 0.1%
 
01< 0.1%
 
71< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common57583100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
41853932.2%
 
11829131.8%
 
61585427.5%
 
341257.2%
 
24800.8%
 
92900.5%
 
52< 0.1%
 
01< 0.1%
 
71< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII57583100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
41853932.2%
 
11829131.8%
 
61585427.5%
 
341257.2%
 
24800.8%
 
92900.5%
 
52< 0.1%
 
01< 0.1%
 
71< 0.1%
 

age
Real number (ℝ≥0)

Distinct57
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.36969171
Minimum18
Maximum75
Zeros0
Zeros (%)0.0%
Memory size449.9 KiB
2021-04-20T17:57:06.172336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile22
Q130
median39
Q350
95-th percentile63
Maximum75
Range57
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.56261499
Coefficient of variation (CV)0.3111892724
Kurtosis-0.7320695661
Mean40.36969171
Median Absolute Deviation (MAD)10
Skewness0.3792367483
Sum2324285
Variance157.8192954
MonotocityNot monotonic
2021-04-20T17:57:06.368605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2918383.2%
 
3517113.0%
 
3416982.9%
 
3716822.9%
 
3016802.9%
 
3616792.9%
 
3816692.9%
 
3316482.9%
 
3116312.8%
 
3215842.8%
 
4015472.7%
 
3915102.6%
 
4114992.6%
 
2714962.6%
 
2814862.6%
 
4214722.6%
 
2614552.5%
 
4414312.5%
 
4314052.4%
 
2513612.4%
 
2412792.2%
 
4712492.2%
 
4512172.1%
 
4612092.1%
 
2212072.1%
 
Other values (32)1993234.6%
 
ValueCountFrequency (%) 
181980.3%
 
192640.5%
 
203690.6%
 
2111682.0%
 
2212072.1%
 
2311582.0%
 
2412792.2%
 
2513612.4%
 
2614552.5%
 
2714962.6%
 
ValueCountFrequency (%) 
752< 0.1%
 
741< 0.1%
 
722< 0.1%
 
712< 0.1%
 
702870.5%
 
693510.6%
 
683410.6%
 
674050.7%
 
664220.7%
 
654760.8%
 

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size449.9 KiB
0
33644 
1
16774 
2
7157 
ValueCountFrequency (%) 
03364458.4%
 
11677429.1%
 
2715712.4%
 
2021-04-20T17:57:06.605859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-20T17:57:06.723240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:57:06.848240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
03364458.4%
 
11677429.1%
 
2715712.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number57575100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
03364458.4%
 
11677429.1%
 
2715712.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Common57575100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
03364458.4%
 
11677429.1%
 
2715712.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII57575100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
03364458.4%
 
11677429.1%
 
2715712.4%
 

country
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size449.9 KiB
EE
22497 
FI
18838 
ES
15947 
SK
 
293
ValueCountFrequency (%) 
EE2249739.1%
 
FI1883832.7%
 
ES1594727.7%
 
SK2930.5%
 
2021-04-20T17:57:07.027392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-20T17:57:07.163639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:57:07.334856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
E6094152.9%
 
F1883816.4%
 
I1883816.4%
 
S1624014.1%
 
K2930.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter115150100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E6094152.9%
 
F1883816.4%
 
I1883816.4%
 
S1624014.1%
 
K2930.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin115150100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E6094152.9%
 
F1883816.4%
 
I1883816.4%
 
S1624014.1%
 
K2930.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII115150100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
E6094152.9%
 
F1883816.4%
 
I1883816.4%
 
S1624014.1%
 
K2930.3%
 

appliedamount
Real number (ℝ≥0)

Distinct501
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2991.880965
Minimum31.9558
Maximum10632
Zeros0
Zeros (%)0.0%
Memory size449.9 KiB
2021-04-20T17:57:07.516937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum31.9558
5-th percentile530
Q11380
median2126
Q34150
95-th percentile8505
Maximum10632
Range10600.0442
Interquartile range (IQR)2770

Descriptive statistics

Standard deviation2365.69706
Coefficient of variation (CV)0.7907056089
Kurtosis2.307490576
Mean2991.880965
Median Absolute Deviation (MAD)1171
Skewness1.531969236
Sum172257546.5
Variance5596522.579
MonotocityNot monotonic
2021-04-20T17:57:07.708765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2125631611.0%
 
530630611.0%
 
425037236.5%
 
212624914.3%
 
318521873.8%
 
300016672.9%
 
1063015492.7%
 
415015172.6%
 
425314562.5%
 
202013772.4%
 
106011532.0%
 
200011001.9%
 
159010891.9%
 
5319191.6%
 
10008551.5%
 
26557741.3%
 
53156561.1%
 
15006251.1%
 
5006241.1%
 
31895240.9%
 
25004930.9%
 
37204650.8%
 
63754520.8%
 
5184370.8%
 
50004320.8%
 
Other values (476)1838831.9%
 
ValueCountFrequency (%) 
31.95582< 0.1%
 
51.12931< 0.1%
 
63.91164< 0.1%
 
70.30281< 0.1%
 
76.6941< 0.1%
 
83.08514< 0.1%
 
95.86757< 0.1%
 
10025< 0.1%
 
102.25861< 0.1%
 
108.64981< 0.1%
 
ValueCountFrequency (%) 
1063219< 0.1%
 
1063015492.7%
 
105261< 0.1%
 
1052511< 0.1%
 
104201< 0.1%
 
104193< 0.1%
 
10415320.1%
 
103132< 0.1%
 
103106< 0.1%
 
1020511< 0.1%
 

interest
Real number (ℝ≥0)

Distinct4884
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.64294468
Minimum3
Maximum264.31
Zeros0
Zeros (%)0.0%
Memory size449.9 KiB
2021-04-20T17:57:07.923985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile15.08
Q128
median35.46
Q357.76
95-th percentile78.61
Maximum264.31
Range261.31
Interquartile range (IQR)29.76

Descriptive statistics

Standard deviation29.97587345
Coefficient of variation (CV)0.6868435132
Kurtosis14.09619997
Mean43.64294468
Median Absolute Deviation (MAD)11.86
Skewness3.10733385
Sum2512742.54
Variance898.5529892
MonotocityNot monotonic
2021-04-20T17:57:08.154128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
59.7346658.1%
 
2821163.7%
 
3115472.7%
 
57.7614132.5%
 
307631.3%
 
58.096751.2%
 
31.56041.0%
 
265180.9%
 
295160.9%
 
324290.7%
 
72.794020.7%
 
37.993970.7%
 
203960.7%
 
31.123550.6%
 
28.523480.6%
 
223460.6%
 
30.53360.6%
 
33.63220.6%
 
333160.5%
 
40.472640.5%
 
75.642520.4%
 
51.042400.4%
 
47.942250.4%
 
57.092220.4%
 
252050.4%
 
Other values (4859)3970369.0%
 
ValueCountFrequency (%) 
31< 0.1%
 
51< 0.1%
 
64< 0.1%
 
71< 0.1%
 
7.271< 0.1%
 
7.314< 0.1%
 
7.319< 0.1%
 
7.432< 0.1%
 
7.482< 0.1%
 
7.4912< 0.1%
 
ValueCountFrequency (%) 
264.311< 0.1%
 
263.961< 0.1%
 
263.592< 0.1%
 
263.151< 0.1%
 
262.912< 0.1%
 
261.353< 0.1%
 
260.0711< 0.1%
 
256.221< 0.1%
 
255.772< 0.1%
 
255.77< 0.1%
 

loanduration
Real number (ℝ≥0)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.66945723
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Memory size449.9 KiB
2021-04-20T17:57:08.380134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q136
median60
Q360
95-th percentile60
Maximum60
Range59
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.80549571
Coefficient of variation (CV)0.3105866224
Kurtosis-0.2333879729
Mean47.66945723
Median Absolute Deviation (MAD)0
Skewness-0.8607715613
Sum2744569
Variance219.2027033
MonotocityNot monotonic
2021-04-20T17:57:08.570629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
603030052.6%
 
361646428.6%
 
4846798.1%
 
2425564.4%
 
1213872.4%
 
1810521.8%
 
93420.6%
 
63140.5%
 
32160.4%
 
302140.4%
 
1330.1%
 
157< 0.1%
 
25< 0.1%
 
81< 0.1%
 
381< 0.1%
 
521< 0.1%
 
41< 0.1%
 
271< 0.1%
 
421< 0.1%
 
ValueCountFrequency (%) 
1330.1%
 
25< 0.1%
 
32160.4%
 
41< 0.1%
 
63140.5%
 
81< 0.1%
 
93420.6%
 
1213872.4%
 
157< 0.1%
 
1810521.8%
 
ValueCountFrequency (%) 
603030052.6%
 
521< 0.1%
 
4846798.1%
 
421< 0.1%
 
381< 0.1%
 
361646428.6%
 
302140.4%
 
271< 0.1%
 
2425564.4%
 
1810521.8%
 

education
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size449.9 KiB
4
19957 
5
15458 
3
13665 
1
5447 
2
3046 
ValueCountFrequency (%) 
41995734.7%
 
51545826.8%
 
31366523.7%
 
154479.5%
 
230465.3%
 
-12< 0.1%
 
2021-04-20T17:57:08.808178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-20T17:57:08.987647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:57:09.192361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.000034737
Min length1

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
41995734.7%
 
51545826.8%
 
31366523.7%
 
154499.5%
 
230465.3%
 
-2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number57575> 99.9%
 
Dash Punctuation2< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
41995734.7%
 
51545826.8%
 
31366523.7%
 
154499.5%
 
230465.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common57577100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
41995734.7%
 
51545826.8%
 
31366523.7%
 
154499.5%
 
230465.3%
 
-2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII57577100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
41995734.7%
 
51545826.8%
 
31366523.7%
 
154499.5%
 
230465.3%
 
-2< 0.1%
 

maritalstatus
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size449.9 KiB
-1
38488 
3
6605 
1
5523 
2
4817 
4
 
1821
ValueCountFrequency (%) 
-13848866.8%
 
3660511.5%
 
155239.6%
 
248178.4%
 
418213.2%
 
53210.6%
 
2021-04-20T17:57:09.382980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-20T17:57:09.516248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:57:09.729253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.668484585
Min length1

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
14401145.8%
 
-3848840.1%
 
366056.9%
 
248175.0%
 
418211.9%
 
53210.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number5757559.9%
 
Dash Punctuation3848840.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
14401176.4%
 
3660511.5%
 
248178.4%
 
418213.2%
 
53210.6%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-38488100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common96063100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
14401145.8%
 
-3848840.1%
 
366056.9%
 
248175.0%
 
418211.9%
 
53210.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII96063100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
14401145.8%
 
-3848840.1%
 
366056.9%
 
248175.0%
 
418211.9%
 
53210.3%
 

nrofdependants
Categorical

MISSING

Distinct10
Distinct (%)0.1%
Missing39009
Missing (%)67.8%
Memory size449.9 KiB
0
10548 
1
4290 
2
2588 
3
 
825
4
 
233
Other values (5)
 
82
ValueCountFrequency (%) 
01054818.3%
 
142907.5%
 
225884.5%
 
38251.4%
 
42330.4%
 
5530.1%
 
712< 0.1%
 
610< 0.1%
 
10Plus5< 0.1%
 
82< 0.1%
 
(Missing)3900967.8%
 
2021-04-20T17:57:09.921342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-20T17:57:10.063299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:57:10.292575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length2.35550152
Min length1

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n7801857.5%
 
a3900928.8%
 
0105537.8%
 
142953.2%
 
225881.9%
 
38250.6%
 
42330.2%
 
553< 0.1%
 
712< 0.1%
 
610< 0.1%
 
P5< 0.1%
 
l5< 0.1%
 
u5< 0.1%
 
s5< 0.1%
 
82< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter11704286.3%
 
Decimal Number1857113.7%
 
Uppercase Letter5< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01055356.8%
 
1429523.1%
 
2258813.9%
 
38254.4%
 
42331.3%
 
5530.3%
 
7120.1%
 
6100.1%
 
82< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n7801866.7%
 
a3900933.3%
 
l5< 0.1%
 
u5< 0.1%
 
s5< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P5100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin11704786.3%
 
Common1857113.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
01055356.8%
 
1429523.1%
 
2258813.9%
 
38254.4%
 
42331.3%
 
5530.3%
 
7120.1%
 
6100.1%
 
82< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n7801866.7%
 
a3900933.3%
 
P5< 0.1%
 
l5< 0.1%
 
u5< 0.1%
 
s5< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII135618100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n7801857.5%
 
a3900928.8%
 
0105537.8%
 
142953.2%
 
225881.9%
 
38250.6%
 
42330.2%
 
553< 0.1%
 
712< 0.1%
 
610< 0.1%
 
P5< 0.1%
 
l5< 0.1%
 
u5< 0.1%
 
s5< 0.1%
 
82< 0.1%
 

employmentstatus
Categorical

Distinct7
Distinct (%)< 0.1%
Missing32
Missing (%)0.1%
Memory size449.9 KiB
-1
38488 
3
16488 
6
 
1076
5
 
600
2
 
532
Other values (2)
 
359
ValueCountFrequency (%) 
-13848866.8%
 
31648828.6%
 
610761.9%
 
56001.0%
 
25320.9%
 
43370.6%
 
022< 0.1%
 
(Missing)320.1%
 
2021-04-20T17:57:10.482115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-20T17:57:10.591502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:57:10.793242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length1.669596179
Min length1

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
-3848840.0%
 
13848840.0%
 
31648817.2%
 
610761.1%
 
56000.6%
 
25320.6%
 
43370.4%
 
n640.1%
 
a32< 0.1%
 
022< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number5754359.9%
 
Dash Punctuation3848840.0%
 
Lowercase Letter960.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
13848866.9%
 
31648828.7%
 
610761.9%
 
56001.0%
 
25320.9%
 
43370.6%
 
022< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n6466.7%
 
a3233.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-38488100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common9603199.9%
 
Latin960.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
-3848840.1%
 
13848840.1%
 
31648817.2%
 
610761.1%
 
56000.6%
 
25320.6%
 
43370.4%
 
022< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n6466.7%
 
a3233.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII96127100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
-3848840.0%
 
13848840.0%
 
31648817.2%
 
610761.1%
 
56000.6%
 
25320.6%
 
43370.4%
 
n640.1%
 
a32< 0.1%
 
022< 0.1%
 
Distinct9
Distinct (%)< 0.1%
Missing315
Missing (%)0.5%
Memory size449.9 KiB
MoreThan5Years
22050 
UpTo1Year
10753 
UpTo5Years
10142 
Retiree
4198 
UpTo2Years
3055 
Other values (4)
7062 
ValueCountFrequency (%) 
MoreThan5Years2205038.3%
 
UpTo1Year1075318.7%
 
UpTo5Years1014217.6%
 
Retiree41987.3%
 
UpTo2Years30555.3%
 
Other26854.7%
 
UpTo3Years23744.1%
 
UpTo4Years16682.9%
 
TrialPeriod3350.6%
 
(Missing)3150.5%
 
2021-04-20T17:57:10.998139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-20T17:57:11.123541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:57:11.326629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length10
Mean length10.86075554
Min length3

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e8770614.0%
 
r7964512.7%
 
a7274211.6%
 
T503778.1%
 
o503778.1%
 
Y500428.0%
 
s392896.3%
 
5321925.1%
 
U279924.5%
 
p279924.5%
 
h247354.0%
 
n226803.6%
 
M220503.5%
 
1107531.7%
 
t68831.1%
 
i48680.8%
 
R41980.7%
 
230550.5%
 
O26850.4%
 
323740.4%
 
416680.3%
 
l3350.1%
 
P3350.1%
 
d3350.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter41758766.8%
 
Uppercase Letter15767925.2%
 
Decimal Number500428.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T5037731.9%
 
Y5004231.7%
 
U2799217.8%
 
M2205014.0%
 
R41982.7%
 
O26851.7%
 
P3350.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e8770621.0%
 
r7964519.1%
 
a7274217.4%
 
o5037712.1%
 
s392899.4%
 
p279926.7%
 
h247355.9%
 
n226805.4%
 
t68831.6%
 
i48681.2%
 
l3350.1%
 
d3350.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
53219264.3%
 
11075321.5%
 
230556.1%
 
323744.7%
 
416683.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin57526692.0%
 
Common500428.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e8770615.2%
 
r7964513.8%
 
a7274212.6%
 
T503778.8%
 
o503778.8%
 
Y500428.7%
 
s392896.8%
 
U279924.9%
 
p279924.9%
 
h247354.3%
 
n226803.9%
 
M220503.8%
 
t68831.2%
 
i48680.8%
 
R41980.7%
 
O26850.5%
 
l3350.1%
 
P3350.1%
 
d3350.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
53219264.3%
 
11075321.5%
 
230556.1%
 
323744.7%
 
416683.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII625308100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e8770614.0%
 
r7964512.7%
 
a7274211.6%
 
T503778.1%
 
o503778.1%
 
Y500428.0%
 
s392896.3%
 
5321925.1%
 
U279924.5%
 
p279924.5%
 
h247354.0%
 
n226803.6%
 
M220503.5%
 
1107531.7%
 
t68831.1%
 
i48680.8%
 
R41980.7%
 
230550.5%
 
O26850.4%
 
323740.4%
 
416680.3%
 
l3350.1%
 
P3350.1%
 
d3350.1%
 

workexperience
Categorical

MISSING

Distinct6
Distinct (%)< 0.1%
Missing38493
Missing (%)66.9%
Memory size449.9 KiB
5To10Years
4114 
15To25Years
3960 
10To15Years
3452 
MoreThan25Years
3425 
2To5Years
2788 
ValueCountFrequency (%) 
5To10Years41147.1%
 
15To25Years39606.9%
 
10To15Years34526.0%
 
MoreThan25Years34255.9%
 
2To5Years27884.8%
 
LessThan2Years13432.3%
 
(Missing)3849366.9%
 
2021-04-20T17:57:11.499020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-20T17:57:11.624023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:57:11.811521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length3
Mean length5.791055145
Min length3

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n8175424.5%
 
a6234318.7%
 
e238507.2%
 
r225076.8%
 
s217686.5%
 
5216996.5%
 
T190825.7%
 
Y190825.7%
 
o177395.3%
 
1149784.5%
 
2115163.5%
 
075662.3%
 
h47681.4%
 
M34251.0%
 
L13430.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter23472970.4%
 
Decimal Number5575916.7%
 
Uppercase Letter4293212.9%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
52169938.9%
 
11497826.9%
 
21151620.7%
 
0756613.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T1908244.4%
 
Y1908244.4%
 
M34258.0%
 
L13433.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n8175434.8%
 
a6234326.6%
 
e2385010.2%
 
r225079.6%
 
s217689.3%
 
o177397.6%
 
h47682.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin27766183.3%
 
Common5575916.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
52169938.9%
 
11497826.9%
 
21151620.7%
 
0756613.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n8175429.4%
 
a6234322.5%
 
e238508.6%
 
r225078.1%
 
s217687.8%
 
T190826.9%
 
Y190826.9%
 
o177396.4%
 
h47681.7%
 
M34251.2%
 
L13430.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII333420100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n8175424.5%
 
a6234318.7%
 
e238507.2%
 
r225076.8%
 
s217686.5%
 
5216996.5%
 
T190825.7%
 
Y190825.7%
 
o177395.3%
 
1149784.5%
 
2115163.5%
 
075662.3%
 
h47681.4%
 
M34251.0%
 
L13430.4%
 

occupationarea
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size449.9 KiB
-1
38519 
1
4471 
7
 
1836
3
 
1779
6
 
1606
Other values (15)
9364 
ValueCountFrequency (%) 
-13851966.9%
 
144717.8%
 
718363.2%
 
317793.1%
 
616062.8%
 
1713732.4%
 
813262.3%
 
911942.1%
 
159881.7%
 
109731.7%
 
167111.2%
 
115481.0%
 
194790.8%
 
144520.8%
 
42860.5%
 
132680.5%
 
182430.4%
 
122340.4%
 
52150.4%
 
2740.1%
 
2021-04-20T17:57:12.014900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-20T17:57:12.203446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.777907078
Min length1

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
14980748.7%
 
-3851937.6%
 
732093.1%
 
623172.3%
 
320472.0%
 
916731.6%
 
815691.5%
 
512031.2%
 
09731.0%
 
47380.7%
 
23080.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number6384462.4%
 
Dash Punctuation3851937.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
14980778.0%
 
732095.0%
 
623173.6%
 
320473.2%
 
916732.6%
 
815692.5%
 
512031.9%
 
09731.5%
 
47381.2%
 
23080.5%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-38519100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common102363100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
14980748.7%
 
-3851937.6%
 
732093.1%
 
623172.3%
 
320472.0%
 
916731.6%
 
815691.5%
 
512031.2%
 
09731.0%
 
47380.7%
 
23080.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII102363100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
14980748.7%
 
-3851937.6%
 
732093.1%
 
623172.3%
 
320472.0%
 
916731.6%
 
815691.5%
 
512031.2%
 
09731.0%
 
47380.7%
 
23080.3%
 
Distinct12
Distinct (%)< 0.1%
Missing117
Missing (%)0.2%
Memory size449.9 KiB
1
16695 
3
14634 
2
9776 
8
6814 
10
3813 
Other values (7)
5726 
ValueCountFrequency (%) 
11669529.0%
 
31463425.4%
 
2977617.0%
 
8681411.8%
 
1038136.6%
 
425074.4%
 
714622.5%
 
68121.4%
 
56031.0%
 
93350.6%
 
05< 0.1%
 
-12< 0.1%
 
(Missing)1170.2%
 
2021-04-20T17:57:12.377672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-20T17:57:12.549546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.070325662
Min length1

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
12051033.3%
 
31463423.7%
 
2977615.9%
 
8681411.1%
 
038186.2%
 
425074.1%
 
714622.4%
 
68121.3%
 
56031.0%
 
93350.5%
 
n2340.4%
 
a1170.2%
 
-2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number6127199.4%
 
Lowercase Letter3510.6%
 
Dash Punctuation2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n23466.7%
 
a11733.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
12051033.5%
 
31463423.9%
 
2977616.0%
 
8681411.1%
 
038186.2%
 
425074.1%
 
714622.4%
 
68121.3%
 
56031.0%
 
93350.5%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common6127399.4%
 
Latin3510.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n23466.7%
 
a11733.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
12051033.5%
 
31463423.9%
 
2977616.0%
 
8681411.1%
 
038186.2%
 
425074.1%
 
714622.4%
 
68121.3%
 
56031.0%
 
93350.5%
 
-2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII61624100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
12051033.3%
 
31463423.7%
 
2977615.9%
 
8681411.1%
 
038186.2%
 
425074.1%
 
714622.4%
 
68121.3%
 
56031.0%
 
93350.5%
 
n2340.4%
 
a1170.2%
 
-2< 0.1%
 

incometotal
Real number (ℝ≥0)

SKEWED

Distinct3357
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1732.391983
Minimum0
Maximum1012019
Zeros2
Zeros (%)< 0.1%
Memory size449.9 KiB
2021-04-20T17:57:12.736552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile500
Q1900
median1300
Q31962
95-th percentile3200
Maximum1012019
Range1012019
Interquartile range (IQR)1062

Descriptive statistics

Standard deviation6823.394839
Coefficient of variation (CV)3.938713007
Kurtosis16823.05198
Mean1732.391983
Median Absolute Deviation (MAD)500
Skewness117.1419579
Sum99742468.45
Variance46558717.13
MonotocityNot monotonic
2021-04-20T17:57:12.946266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
120024184.2%
 
100018543.2%
 
150017553.0%
 
200015052.6%
 
130014802.6%
 
110014582.5%
 
180013402.3%
 
80012162.1%
 
140011582.0%
 
90011191.9%
 
160011131.9%
 
250010601.8%
 
17009401.6%
 
22009341.6%
 
7008111.4%
 
19007491.3%
 
21007311.3%
 
23006581.1%
 
30006311.1%
 
24006011.0%
 
6006001.0%
 
5005571.0%
 
8505040.9%
 
7504700.8%
 
28004630.8%
 
Other values (3332)3145054.6%
 
ValueCountFrequency (%) 
02< 0.1%
 
0.332< 0.1%
 
15< 0.1%
 
1.041< 0.1%
 
1.051< 0.1%
 
1.181< 0.1%
 
1.23< 0.1%
 
1.43< 0.1%
 
1.61< 0.1%
 
1.751< 0.1%
 
ValueCountFrequency (%) 
10120192< 0.1%
 
2800001< 0.1%
 
2350001< 0.1%
 
2285501< 0.1%
 
2200001< 0.1%
 
1609741< 0.1%
 
1220261< 0.1%
 
1200453< 0.1%
 
1179001< 0.1%
 
1084761< 0.1%
 

incomefromsalary
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct2628
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean410.2201832
Minimum0
Maximum228400
Zeros39497
Zeros (%)68.6%
Memory size449.9 KiB
2021-04-20T17:57:13.156308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3600
95-th percentile1930
Maximum228400
Range228400
Interquartile range (IQR)600

Descriptive statistics

Standard deviation1353.807862
Coefficient of variation (CV)3.300198081
Kurtosis14202.1022
Mean410.2201832
Median Absolute Deviation (MAD)0
Skewness88.63885754
Sum23618427.05
Variance1832795.726
MonotocityNot monotonic
2021-04-20T17:57:13.343809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03949768.6%
 
10003830.7%
 
8003340.6%
 
12003250.6%
 
7002750.5%
 
15002620.5%
 
11002550.4%
 
6002370.4%
 
5002270.4%
 
13002250.4%
 
9002240.4%
 
20002090.4%
 
18001930.3%
 
14001620.3%
 
16001600.3%
 
7501550.3%
 
17001550.3%
 
8501540.3%
 
6501410.2%
 
22001250.2%
 
5501230.2%
 
25001210.2%
 
4001110.2%
 
19001060.2%
 
4501030.2%
 
Other values (2603)1331323.1%
 
ValueCountFrequency (%) 
03949768.6%
 
11< 0.1%
 
301< 0.1%
 
481< 0.1%
 
491< 0.1%
 
631< 0.1%
 
742< 0.1%
 
761< 0.1%
 
791< 0.1%
 
891< 0.1%
 
ValueCountFrequency (%) 
2284001< 0.1%
 
804001< 0.1%
 
300001< 0.1%
 
270001< 0.1%
 
235001< 0.1%
 
180002< 0.1%
 
163001< 0.1%
 
160006< 0.1%
 
157001< 0.1%
 
155001< 0.1%
 

incomefromothers
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct1289
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.34181329
Minimum0
Maximum25000
Zeros50943
Zeros (%)88.5%
Memory size449.9 KiB
2021-04-20T17:57:13.530565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile281.3
Maximum25000
Range25000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation339.3319119
Coefficient of variation (CV)6.131564756
Kurtosis1342.76671
Mean55.34181329
Median Absolute Deviation (MAD)0
Skewness26.59696623
Sum3186304.9
Variance115146.1464
MonotocityNot monotonic
2021-04-20T17:57:13.735039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05094388.5%
 
193360.6%
 
382950.5%
 
1002730.5%
 
2002270.4%
 
502250.4%
 
3001400.2%
 
451050.2%
 
400950.2%
 
95900.2%
 
150900.2%
 
500760.1%
 
250740.1%
 
90660.1%
 
57600.1%
 
104530.1%
 
1000530.1%
 
140520.1%
 
219500.1%
 
350490.1%
 
600460.1%
 
210400.1%
 
64360.1%
 
115310.1%
 
220300.1%
 
Other values (1264)40407.0%
 
ValueCountFrequency (%) 
05094388.5%
 
22< 0.1%
 
41< 0.1%
 
61< 0.1%
 
72< 0.1%
 
81< 0.1%
 
101< 0.1%
 
122< 0.1%
 
153< 0.1%
 
161< 0.1%
 
ValueCountFrequency (%) 
250001< 0.1%
 
220001< 0.1%
 
183801< 0.1%
 
180001< 0.1%
 
135004< 0.1%
 
100001< 0.1%
 
89001< 0.1%
 
80005< 0.1%
 
70001< 0.1%
 
67001< 0.1%
 

existingliabilities
Real number (ℝ≥0)

ZEROS

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.578671298
Minimum0
Maximum36
Zeros14916
Zeros (%)25.9%
Memory size449.9 KiB
2021-04-20T17:57:13.922837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum36
Range36
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.124292141
Coefficient of variation (CV)1.211589916
Kurtosis7.376285361
Mean2.578671298
Median Absolute Deviation (MAD)2
Skewness2.232888238
Sum148467
Variance9.761201382
MonotocityNot monotonic
2021-04-20T17:57:14.110341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%) 
01491625.9%
 
11344723.4%
 
2888815.4%
 
355879.7%
 
439836.9%
 
527954.9%
 
620293.5%
 
715262.7%
 
811322.0%
 
98581.5%
 
105841.0%
 
114990.9%
 
123470.6%
 
132680.5%
 
142040.4%
 
151460.3%
 
16940.2%
 
17770.1%
 
18540.1%
 
19360.1%
 
2024< 0.1%
 
2117< 0.1%
 
2217< 0.1%
 
2311< 0.1%
 
267< 0.1%
 
Other values (11)290.1%
 
ValueCountFrequency (%) 
01491625.9%
 
11344723.4%
 
2888815.4%
 
355879.7%
 
439836.9%
 
527954.9%
 
620293.5%
 
715262.7%
 
811322.0%
 
98581.5%
 
ValueCountFrequency (%) 
361< 0.1%
 
341< 0.1%
 
331< 0.1%
 
322< 0.1%
 
313< 0.1%
 
301< 0.1%
 
291< 0.1%
 
281< 0.1%
 
277< 0.1%
 
267< 0.1%
 

liabilitiestotal
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct14705
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean680.9745884
Minimum0
Maximum12400000
Zeros15413
Zeros (%)26.8%
Memory size449.9 KiB
2021-04-20T17:57:14.313462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median283
Q3637
95-th percentile1520
Maximum12400000
Range12400000
Interquartile range (IQR)637

Descriptive statistics

Standard deviation51697.12185
Coefficient of variation (CV)75.91637445
Kurtosis57480.70763
Mean680.9745884
Median Absolute Deviation (MAD)283
Skewness239.6549801
Sum39207111.93
Variance2672592407
MonotocityNot monotonic
2021-04-20T17:57:14.532212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01541326.8%
 
2508451.5%
 
3507161.2%
 
3005020.9%
 
2004390.8%
 
5004250.7%
 
4003970.7%
 
1003230.6%
 
6003130.5%
 
4502860.5%
 
1502700.5%
 
7001710.3%
 
501630.3%
 
115.841590.3%
 
5501580.3%
 
8001440.3%
 
237.841400.2%
 
6501260.2%
 
3601140.2%
 
1201130.2%
 
1801110.2%
 
2701000.2%
 
1000960.2%
 
113.04960.2%
 
750960.2%
 
Other values (14680)3585962.3%
 
ValueCountFrequency (%) 
01541326.8%
 
0.421< 0.1%
 
12< 0.1%
 
31< 0.1%
 
52< 0.1%
 
61< 0.1%
 
72< 0.1%
 
7.881< 0.1%
 
83< 0.1%
 
1011< 0.1%
 
ValueCountFrequency (%) 
124000001< 0.1%
 
1725101< 0.1%
 
145041.531< 0.1%
 
145021.151< 0.1%
 
1450001< 0.1%
 
69644.091< 0.1%
 
574691< 0.1%
 
35646.51< 0.1%
 
33023.021< 0.1%
 
330001< 0.1%
 

debttoincome
Real number (ℝ≥0)

ZEROS

Distinct6156
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.921500304
Minimum0
Maximum110.44
Zeros38935
Zeros (%)67.6%
Memory size449.9 KiB
2021-04-20T17:57:14.774462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314.9
95-th percentile52.163
Maximum110.44
Range110.44
Interquartile range (IQR)14.9

Descriptive statistics

Standard deviation17.65576113
Coefficient of variation (CV)1.779545491
Kurtosis2.122524206
Mean9.921500304
Median Absolute Deviation (MAD)0
Skewness1.773585391
Sum571230.38
Variance311.7259009
MonotocityNot monotonic
2021-04-20T17:57:14.985882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03893567.6%
 
2021< 0.1%
 
2413< 0.1%
 
69.8913< 0.1%
 
29.3312< 0.1%
 
69.9812< 0.1%
 
69.911< 0.1%
 
23.2510< 0.1%
 
30.4610< 0.1%
 
12.1510< 0.1%
 
8.5810< 0.1%
 
17.6610< 0.1%
 
17.3710< 0.1%
 
14.1410< 0.1%
 
21.2410< 0.1%
 
17.510< 0.1%
 
21.210< 0.1%
 
17.1810< 0.1%
 
14.2410< 0.1%
 
8.510< 0.1%
 
69.8410< 0.1%
 
1610< 0.1%
 
20.6210< 0.1%
 
11.7110< 0.1%
 
21.9110< 0.1%
 
Other values (6131)1837831.9%
 
ValueCountFrequency (%) 
03893567.6%
 
0.41< 0.1%
 
0.541< 0.1%
 
0.591< 0.1%
 
0.631< 0.1%
 
0.681< 0.1%
 
0.741< 0.1%
 
0.832< 0.1%
 
0.851< 0.1%
 
0.891< 0.1%
 
ValueCountFrequency (%) 
110.441< 0.1%
 
83.331< 0.1%
 
79.961< 0.1%
 
771< 0.1%
 
76.641< 0.1%
 
75.251< 0.1%
 
73.381< 0.1%
 
72.941< 0.1%
 
72.671< 0.1%
 
71.751< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size449.9 KiB
0
57570 
1
 
5
ValueCountFrequency (%) 
057570> 99.9%
 
15< 0.1%
 
2021-04-20T17:57:15.188190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

noofpreviousloansbeforeloan
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3171341728
Minimum0
Maximum12
Zeros43554
Zeros (%)75.6%
Memory size449.9 KiB
2021-04-20T17:57:15.294115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6469658058
Coefficient of variation (CV)2.040038133
Kurtosis18.74201244
Mean0.3171341728
Median Absolute Deviation (MAD)0
Skewness3.012976342
Sum18259
Variance0.4185647539
MonotocityNot monotonic
2021-04-20T17:57:15.453996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
04355475.6%
 
11072218.6%
 
226214.6%
 
35681.0%
 
4520.1%
 
522< 0.1%
 
614< 0.1%
 
78< 0.1%
 
85< 0.1%
 
93< 0.1%
 
122< 0.1%
 
112< 0.1%
 
102< 0.1%
 
ValueCountFrequency (%) 
04355475.6%
 
11072218.6%
 
226214.6%
 
35681.0%
 
4520.1%
 
522< 0.1%
 
614< 0.1%
 
78< 0.1%
 
85< 0.1%
 
93< 0.1%
 
ValueCountFrequency (%) 
122< 0.1%
 
112< 0.1%
 
102< 0.1%
 
93< 0.1%
 
85< 0.1%
 
78< 0.1%
 
614< 0.1%
 
522< 0.1%
 
4520.1%
 
35681.0%
 

amountofpreviousloansbeforeloan
Real number (ℝ≥0)

ZEROS

Distinct1681
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean744.5462582
Minimum0
Maximum27315
Zeros43554
Zeros (%)75.6%
Memory size449.9 KiB
2021-04-20T17:57:15.625525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4250
Maximum27315
Range27315
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1767.045129
Coefficient of variation (CV)2.373318124
Kurtosis14.63423919
Mean744.5462582
Median Absolute Deviation (MAD)0
Skewness3.317018268
Sum42867250.82
Variance3122448.486
MonotocityNot monotonic
2021-04-20T17:57:15.797405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04355475.6%
 
212515952.8%
 
53013862.4%
 
425010911.9%
 
10606761.2%
 
31856051.1%
 
15904610.8%
 
20203500.6%
 
26552140.4%
 
30002040.4%
 
63751810.3%
 
10001740.3%
 
20001730.3%
 
21261430.2%
 
5001310.2%
 
53151280.2%
 
106301150.2%
 
15001110.2%
 
2230950.2%
 
1380900.2%
 
635850.1%
 
2335850.1%
 
1275840.1%
 
850820.1%
 
1700790.1%
 
Other values (1656)56839.9%
 
ValueCountFrequency (%) 
04355475.6%
 
31.95581< 0.1%
 
63.90251< 0.1%
 
63.91182< 0.1%
 
95.861< 0.1%
 
95.86751< 0.1%
 
95.8681< 0.1%
 
1007< 0.1%
 
127.811< 0.1%
 
127.82281< 0.1%
 
ValueCountFrequency (%) 
273151< 0.1%
 
244451< 0.1%
 
233751< 0.1%
 
212601< 0.1%
 
209991< 0.1%
 
201951< 0.1%
 
193451< 0.1%
 
191251< 0.1%
 
190201< 0.1%
 
187481< 0.1%
 

priorrepayments
Real number (ℝ≥0)

ZEROS

Distinct12521
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.1424571
Minimum0
Maximum28093.12
Zeros43733
Zeros (%)76.0%
Memory size449.9 KiB
2021-04-20T17:57:16.017270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile741.53
Maximum28093.12
Range28093.12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation793.5813263
Coefficient of variation (CV)4.531061967
Kurtosis198.6750649
Mean175.1424571
Median Absolute Deviation (MAD)0
Skewness11.73500316
Sum10083826.97
Variance629771.3214
MonotocityNot monotonic
2021-04-20T17:57:16.220277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04373376.0%
 
118.9912< 0.1%
 
31.811< 0.1%
 
37.19< 0.1%
 
26.59< 0.1%
 
308.297< 0.1%
 
357.657< 0.1%
 
224.347< 0.1%
 
214.117< 0.1%
 
378.87< 0.1%
 
414.067< 0.1%
 
241.046< 0.1%
 
152.746< 0.1%
 
230.726< 0.1%
 
42.46< 0.1%
 
227.756< 0.1%
 
159.566< 0.1%
 
364.76< 0.1%
 
447.346< 0.1%
 
175.296< 0.1%
 
149.336< 0.1%
 
329.446< 0.1%
 
207.295< 0.1%
 
122.415< 0.1%
 
315.345< 0.1%
 
Other values (12496)1367823.8%
 
ValueCountFrequency (%) 
04373376.0%
 
11.83331< 0.1%
 
13.511< 0.1%
 
14.81< 0.1%
 
15.31< 0.1%
 
15.741< 0.1%
 
15.861< 0.1%
 
15.94< 0.1%
 
15.971< 0.1%
 
16.521< 0.1%
 
ValueCountFrequency (%) 
28093.121< 0.1%
 
22316.011< 0.1%
 
22109.11< 0.1%
 
21966.271< 0.1%
 
20436.711< 0.1%
 
19571.271< 0.1%
 
19208.71< 0.1%
 
190701< 0.1%
 
18839.31< 0.1%
 
18499.181< 0.1%
 

previousearlyrepaymentsbeforeloan
Real number (ℝ≥0)

MISSING
ZEROS

Distinct292
Distinct (%)1.1%
Missing30802
Missing (%)53.5%
Infinite0
Infinite (%)0.0%
Mean202.6368356
Minimum0
Maximum41755
Zeros25245
Zeros (%)43.8%
Memory size449.9 KiB
2021-04-20T17:57:16.437020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile600
Maximum41755
Range41755
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1250.107246
Coefficient of variation (CV)6.169200391
Kurtosis272.7673075
Mean202.6368356
Median Absolute Deviation (MAD)0
Skewness12.83816713
Sum5425196
Variance1562768.125
MonotocityNot monotonic
2021-04-20T17:57:16.665383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02524543.8%
 
30001140.2%
 
5001060.2%
 
10001030.2%
 
2000850.1%
 
1500690.1%
 
530500.1%
 
10000490.1%
 
2500420.1%
 
4000420.1%
 
5000380.1%
 
4250360.1%
 
2125340.1%
 
60024< 0.1%
 
350023< 0.1%
 
90019< 0.1%
 
120019< 0.1%
 
318519< 0.1%
 
600018< 0.1%
 
80018< 0.1%
 
450016< 0.1%
 
700016< 0.1%
 
550015< 0.1%
 
800014< 0.1%
 
110014< 0.1%
 
Other values (267)5450.9%
 
(Missing)3080253.5%
 
ValueCountFrequency (%) 
02524543.8%
 
1901< 0.1%
 
4101< 0.1%
 
4301< 0.1%
 
4651< 0.1%
 
5001060.2%
 
5101< 0.1%
 
5182< 0.1%
 
530500.1%
 
5315< 0.1%
 
ValueCountFrequency (%) 
417554< 0.1%
 
287041< 0.1%
 
265003< 0.1%
 
231701< 0.1%
 
212603< 0.1%
 
200001< 0.1%
 
197001< 0.1%
 
195001< 0.1%
 
182801< 0.1%
 
180001< 0.1%
 

previousearlyrepaymentscountbeforeloan
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03600521059
Minimum0
Maximum9
Zeros55985
Zeros (%)97.2%
Memory size449.9 KiB
2021-04-20T17:57:16.893077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2517304269
Coefficient of variation (CV)6.991499918
Kurtosis216.576466
Mean0.03600521059
Median Absolute Deviation (MAD)0
Skewness11.61211034
Sum2073
Variance0.06336820784
MonotocityNot monotonic
2021-04-20T17:57:17.045817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
05598597.2%
 
112802.2%
 
22170.4%
 
3570.1%
 
417< 0.1%
 
57< 0.1%
 
65< 0.1%
 
84< 0.1%
 
72< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
05598597.2%
 
112802.2%
 
22170.4%
 
3570.1%
 
417< 0.1%
 
57< 0.1%
 
65< 0.1%
 
72< 0.1%
 
84< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
91< 0.1%
 
84< 0.1%
 
72< 0.1%
 
65< 0.1%
 
57< 0.1%
 
417< 0.1%
 
3570.1%
 
22170.4%
 
112802.2%
 
05598597.2%
 

Interactions

2021-04-20T17:56:18.310262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:18.517301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:18.701314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:18.881303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:19.065501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:19.240458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:19.415498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:19.590482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:19.766771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:19.955523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:20.119487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:20.292487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:20.474980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:20.658033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:20.826032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:20.991076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:21.157028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:21.326028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:21.493028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:21.665047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:21.829047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:21.996047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:22.167046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:22.342046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:22.520046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:22.686066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:22.858112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:23.040962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:23.235970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:23.407959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:23.567919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:23.731919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:23.898920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:24.063923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:24.233925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:24.395977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:24.557976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:24.725977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:24.899977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:25.075976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:25.241976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:25.410977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:25.591013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:25.777178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:25.938179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:26.101374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:26.278368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:26.457374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:26.633378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:26.813384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:26.986378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:27.150382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:27.321382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:27.499382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:27.678380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:27.848434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:28.021434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:28.201432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:28.393400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:28.577413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:28.766400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:28.945427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:29.113468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:29.276468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:29.442470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:29.600476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:29.759471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:29.924667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:30.095668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:30.269659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:30.430667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:30.596669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:30.768666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:30.943666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:31.100662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:31.261472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:31.425465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:31.590474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:31.753466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:31.922462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:32.082472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:32.243465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:32.400074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:32.564973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:32.752528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:32.918020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:33.082023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:33.251064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:33.426069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:33.585059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:33.744081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:33.910059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:34.076864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:34.242864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:34.396502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:34.568389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:34.724624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:34.896715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:35.068592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:35.241966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:35.391611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:35.564988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:35.736871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:35.910394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:36.082534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:36.239642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:36.411520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:36.583395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:36.747561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:36.934646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:37.106647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:37.278646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:37.454646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:37.635642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:37.829703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:38.018768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:38.207811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:38.405769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:38.591768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:38.763767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:38.947773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:39.135809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:39.312813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:39.490816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:39.680807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:39.865770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:40.052769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:40.236816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:40.427816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:40.617813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:40.803771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:40.995809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:41.193811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:41.387027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:41.582528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:41.796968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:41.976643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:42.187907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:42.365849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:42.542272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:42.715310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:42.920427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:43.111463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:43.306425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:43.537429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:43.742468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:43.939428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:44.132478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:44.333426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:44.516321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:44.735345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:44.962623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:45.229004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:45.451437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:45.657297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:45.850302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:46.045303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:46.246423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:46.433397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:46.620896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:46.808397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:46.972568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:47.166165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:47.354604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:47.526482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:47.699330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:47.896906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:48.144243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:48.424764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:48.643325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:48.847721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:49.061674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:49.343393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:49.603218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:49.839965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:50.030437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:50.248040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:50.495354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:50.716101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:50.900207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:51.089433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:51.276889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:51.464833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:51.652803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:51.847431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:52.053726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:52.227735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:52.415391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:52.619047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:52.827615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:53.016232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:53.217262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:53.410942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:53.614079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:53.818447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:54.044539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:54.251327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:54.449766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:54.636446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:54.812290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:54.985291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:55.141547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:55.322296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:55.496615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:55.696829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:55.895242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:56.063951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:56.239122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:56.427427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:56.600512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:56.757683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:56.930227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:57.095961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:57.290320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:57.496288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:57.692534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:57.879187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:58.105790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:58.317396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:58.509417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:58.713907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:58.905342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:59.106647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:59.303836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:56:59.475956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-04-20T17:57:17.258319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-20T17:57:17.690374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-20T17:57:18.106990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-20T17:57:18.558617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-20T17:57:19.093249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-20T17:57:00.112822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:57:02.221868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:57:03.013597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T17:57:03.445408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

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600E25D5BF-6FE5-45E9-B26A-9D440093286Danne482010-03-25 08:55:001481EE511.293240.035113UpTo3Years15To25Years16NaN12100.09500.02600.032250.00.0000.0000.00.00
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Last rows

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